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Hyperpixel Flow: Semantic Correspondence With Multi-Layer Neural Features
AbstractEstablishing visual correspondences under large intra-class variations requires analyzing images at different levels, from features linked to semantics and context to local patterns, while being invariant to instance-specific details. To tackle these challenges, we represent images by "hyperpixels" that leverage a small number of relevant features selected among early to late layers of a convolutional neural network. Taking advantage of the condensed features of hyperpixels, we develop an effective real-time matching algorithm based on Hough geometric voting. The proposed method, hyperpixel flow, sets a new state of the art on three standard benchmarks as well as a new dataset, SPair-71k, which contains a significantly larger number of image pairs than existing datasets, with more accurate and richer annotations for in-depth analysis.
Related Material
[pdf][bibtex]@InProceedings{Min_2019_ICCV,
author = {Min, Juhong and Lee, Jongmin and Ponce, Jean and Cho, Minsu},
title = {Hyperpixel Flow: Semantic Correspondence With Multi-Layer Neural Features},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}
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